Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Socioeconomic disparities in site-specific cancer incidence and mortality: Golestan cohort study.

BMJ public health·2026
Same author

The association between dietary total, animal and plant protein and macronutrient substitution; and the risk of all-cause, cardiovascular or cancer mortality in the Golestan cohort study.

BMC public health·2026
Same author

‌Biomarkers of opioids, volatile organic compounds, and polycyclic aromatic hydrocarbons and lung cancer incidence among opium users.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Estimating opium use prevalence at the national and provincial levels in Iran: a modelling study.

Scientific reports·2026
Same author

The association between waterpipe smoking and head and neck squamous cell carcinoma: A multicenter case-control study in Iran.

International journal of cancer·2025
Same author

Regular use of pharmaceutical opioids and subsequent risk of cancer: a prospective cohort study and Mendelian randomization analysis.

EClinicalMedicine·2025
Same journal

Association of Serum Lipids with 10-Year CVD and All-Cause Mortality in Iranian Adults: A Prospective Cohort Study.

Archives of Iranian medicine·2026
Same journal

Pancreatic PEComa: Case Report of an Extremely Rare Tumor.

Archives of Iranian medicine·2026
Same journal

Exploring the Role of Central Venous Pressure in Cardiac Surgery-Associated Acute Kidney Injury: A Comprehensive Scoping Review.

Archives of Iranian medicine·2026
Same journal

Medical Treatment of Hyperthyroidism; Efficacy and Safety Considerations.

Archives of Iranian medicine·2026
Same journal

Performance of ChatGPT and Gemini Compared with Emergency Physicians in NSTEMI Cases: A Prospective Cross-sectional Study.

Archives of Iranian medicine·2026
Same journal

Stent Patency and Survival after PTBD and Biliary Stenting for Pancreatic Cancer: A 5-Year Retrospective Cohort Study.

Archives of Iranian medicine·2026
See all related articles

Related Experiment Video

Updated: May 20, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Confounding variables in epidemiologic studies: basics and beyond.

Farin Kamangar1

  • 1School of Community Health and Policy, Morgan State University, Baltimore, MD, USA. farin.kamangar@morgan.edu

Archives of Iranian Medicine
|July 26, 2012
PubMed
Summary
This summary is machine-generated.

Understanding confounding in epidemiology is crucial for accurate research. This article defines confounders, explains how to identify and manage them, and differentiates them from mediators and effect modifiers.

Related Experiment Videos

Last Updated: May 20, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Confounding is a major challenge in epidemiological research.
  • Misinterpreting confounding can lead to biased results and incorrect conclusions.

Purpose of the Study:

  • To define and categorize confounders in epidemiological studies.
  • To outline methods for identifying and addressing confounding.
  • To clarify the distinctions between confounders, mediators, and effect modifiers.

Main Methods:

  • Literature review and synthesis of epidemiological principles.
  • Explanation of statistical and design strategies for confounding control.
  • Comparative analysis of related concepts.

Main Results:

  • Provides a clear definition and classification of confounders.
  • Discusses various methods for confounding identification and adjustment, including their pros and cons.
  • Highlights key differences between confounders, mediators, and effect modifiers.

Conclusions:

  • Proper identification and management of confounding are essential for valid epidemiological findings.
  • Distinguishing confounders from mediators and effect modifiers is critical for accurate interpretation of study results.